Name
Title | ||
|---|---|---|
Stacked Multiscale Feature Learning for Domain Independent Medical Image Segmentation. |
Abstract | ||
|---|---|---|
In this work we propose a feature-based segmentation approach that is domain independent. While most existing approaches are based on application-specific hand-crafted features, we propose a framework for learning features from data itself at multiple scales and depth. Our features can be easily integrated into classifiers or energy-based segmentation algorithms. We test the performance of our proposed method on two MICCAI grand challenges, obtaining the top score on VESSEL12 and competitive performance on BRATS2012. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10581-9_4 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Vessel segmentation,Computer vision,Dictionary learning,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Grand Challenges,Artificial intelligence,Feature learning | Conference | 8679 |
ISSN | Citations | PageRank |
0302-9743 | 3 | 0.46 |
References | Authors | |
19 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kiros, Ryan | 1 | 2265 | 94.80 |
| Karteek Popuri | 2 | 59 | 8.80 |
| Dana Cobzas | 3 | 207 | 22.19 |
| Martin Jägersand | 4 | 334 | 43.10 |